Development of Wavelet Stochastic Estimation for Identifying the Contribution of Turbulent Structures to the Sound Field of Shear Flows

Fundamental understanding of noise generation and the development of noise reduction technology requires the development of tools that can analyze simultaneously the relationship between the turbulent flow field and the pressure field both near and far. In this proposal we will demonstrate how Wavelet Stochastic Estimation (WSE) is the most optimal method for correlating the source region to the sound field when using a microphone array and Particle Image Velocimetry. WSE first transforms the far-field pressure signal into the wavelet domain which then enables both temporal and frequency information to be correlated with the flow field. By adding the frequency information to the correlations, it becomes easier to extract the contribution from the large-scale structures and thus relate their dynamics to noise generation. We also demonstrate how WSE can be used with flow structure identification methods, such as the Proper Orthogonal Decomposition (POD), to further improve the link between the sound field and the turbulent flow field. The proposed technology supports the Fundamental Aeronautics Program by improving noise prediction and measurement methods. The technology will be available for both subsonic and supersonic vehicles, with particular emphasis on noise sources generated from shear flows.

Wavelet Stochastic Estimation is an analytical tool for optimizing the transfer function from one set of data to a second set of data. For acoustics, the first set of data is often the source field and the second set of data is the sound field. However, any application looking to understand the transfer of data from one region to another would find application with Stochastic Estimation. Also, using our advanced technique would add the benefit of including frequency information in this understanding. Using Stochastic Estimation for relating the turbulent structures in a shear layer to the radiate sound would find non-NASA commercial applications with GE Aviation and GE Global Research, Boeing, Air Force, and NAVY to name a few. The ability to use this method with both experimental and computational databases further demonstrates the flexibility and feasibility of this product. Validation of computational databases with experimental databases is often done with time-averaged quantities. The WSE method could be used to compare the large-scale dynamics captured in a computational database to an experimental database.

The Wavelet Stochastic Estimation method has the ability to support the Fundamental Aeronautics Program by improving noise prediction and measurement methods for subsonic and supersonic vehicles, including jet, and airframe noise sources. We particularly address the need for innovative source identification techniques for engine (e.g., fan, jet, combustor, or turbine noise) and for airframe (e.g., landing gear, high lift systems) noise sources, including turbulence details related to flow-induced noise typical of jets, separated flow regions, vortices, shear layers, etc. The Wavelet Stochastic Estimation method for source identification will be demonstrated in a shear flow surrounding an exhausting subsonic jet. However, the method is directly applicable to any flow-induced noise since the sources for jets, separated flows, vortices and shear layers can all be measured using PIV and their sound fields can be measured using microphone arrays. We also believe the development of the current tools could expand into the structures community since the transfer of vibrations would use the same concept as the transfer of sound.

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